To understand the core concepts and methodologies involved in data engineering including data ingestion, transformation, and storage. 2. To design and implement scalable data pipelines capable of handling large volumes of structured and unstructured data. 3. To explore and utilize modern data engineering tools and frameworks such as Apache Hadoop, Apache Spark, Kafka, and cloud-based storage solutions. 4. To ensure data quality and integrity throughout the pipeline by implementing validation and cleansing techniques. 5. To learn best practices in optimizing data workflows for performance and cost efficiency in a real-world big data environment. 6. To develop an end-to-end data engineering solution that supports advanced analytics and reporting needs. 7. To document the pipeline design and implementation processes comprehensively to facilitate future maintenance and upgrades.
Conduct a thorough literature review on current data engineering practices, tools, and technologies. 2. Identify a use case that requires processing and analyzing large datasets, such as social media data or sensor data. 3. Design a data pipeline architecture that addresses data ingestion, processing, transformation, and storage needs. 4. Implement the designed pipeline using suitable tools like Apache Spark for data processing and Kafka for streaming data ingestion. 5. Perform data cleaning and validation to ensure the reliability and accuracy of the dataset used within the pipeline. 6. Test the pipeline's functionality, scalability, and performance under different data loads and optimize accordingly. 7. Create documentation covering the pipeline’s architecture, technologies used, challenges faced, and resolutions. 8. Prepare a final report and presentation demonstrating the project outcomes and reflecting on the experience gained throughout the development process.
